Analysis Of Indonesian Daily Test Results Using the C4.5 Decision Tree Algorithm - Bambang Sugito; Nelviony Parhusip; Ibrahim Ibrahim; Maida Indrayani (Sean Institute, 2024)
This study aims to analyse the results of Indonesian daily tests using the Decision Tree C4.5 algorithm in improving the effectiveness of evaluating student learning outcomes. The data used is the daily test results of students of class X SMK Panca Budi in the academic year 2023/2024. The research process involves the stages of data collection, preprocessing to clean the data, and modelling using RapidMiner software. Decision Tree C4.5 algorithm was used to predict grade improvement based on students' STATUS score. The results showed that the higher the STATUS score, the greater the increase in grades obtained by students. This decision tree model can be used to evaluate student progress and provide clearer insights for the development of technology-based learning methods. This research contributes to the utilisation of data mining technology to improve educational evaluation, although the results are limited to a sample of grade X students at SMK Panca Budi. Further research with larger samples and additional variables is expected to provide more representative results.